March 29, 2024, 4:42 a.m. | Yuqing Wang, Mika V. Mantyl\"a, Serge Demeyer, Mutlu Beyazit, Joanna Kisaakye, Jesse Nyyss\"ol\"a

cs.LG updates on

arXiv:2403.18998v1 Announce Type: cross
Abstract: Microservice-based systems (MSS) may experience failures in various fault categories due to their complex and dynamic nature. To effectively handle failures, AIOps tools utilize trace-based anomaly detection and root cause analysis. In this paper, we propose a novel framework for few-shot abnormal trace classification for MSS. Our framework comprises two main components: (1) Multi-Head Attention Autoencoder for constructing system-specific trace representations, which enables (2) Transformer Encoder-based Model-Agnostic Meta-Learning to perform effective and efficient few-shot learning …

abstract aiops analysis anomaly anomaly detection arxiv classification cs.lg detection dynamic experience few-shot framework nature novel paper root cause analysis systems tools type

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